A1: Climate Variability and Predictability

Within this research topic, we investigate the variability and the predictability of climate taking both internal variations and external forcing into account. Internal variations determine climate predictability on a wide range of scales: While slow variations constitute predictability, short-term unpredictable fluctuations can notably limit predictability. External forcing factors affect climate predictability through the different climate responses to these perturbations. Our research is primarily based on numerical climate simulations.

Objectives

Work within A1 investigates the variability and predictability of climate from three perspectives:

  1. Climate predictability affected by and resulting from internal variability: we aim to identify the masking effects of unpredictable fluctuations and their role for large-scale dynamics and investigate the mechanisms and deterministic time scales of predictable slow climate component.
  2. Quantifying and reducing uncertainties relevant for predictability: we aim to develop a parametrization of ocean mixing that depends on the climate state.
  3. Predictability originating from responses to perturbations in external forcing: we analyse past millennium simulations and apply methods of non-equilibrium statistical mechanics to the climate system.

Latest A1 Publications

  • Blender, R., & Badin, G. (2017). Construction of Hamiltonian and Nambu forms for the shallow water equations. Fluids, 2: 24. doi:10.3390/fluids2020024.
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  • Graves, T., Franzke, C., Watkins, N. W., Gramacy, R. B., & Tindale, E. (2017). Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models. Physica A: Statistical Mechanics and its Applications, 473, 60-71. doi:10.1016/j.physa.2017.01.028.
  • Tian, F., von Storch, J. S., & Hertwig, E. (2017). Air–sea fluxes in a climate model using hourly coupling between the atmospheric and the oceanic components. Climate Dynamics, 48, 2819-2836. doi:10.1007/s00382-016-3228-y.
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  • Franzke, C. (2017). Extremes in dynamic-stochastic systems. Chaos, 27(1). doi:10.1063/1.4973541.
  • Franzke, C., & O'Kane, T. (Eds.). (2017). Nonlinear and Stochastic Climate. Cambridge: Cambridge University Press.